I am looking for a skilled programmer analyst to create a Jupyter Notebook that performs sentiment analysis on tweets related to a specific topic of interest. The goal is to collect Twitter data, clean and process it, analyze sentiment trends, and visualize the results to answer key questions about public opinion on the chosen topic. Scope of Work: The freelancer will: Data Collection: Gather tweets related to a specific topic (to be finalized with the freelancer). Ensure data includes relevant fields such as tweet text, timestamp, and user information. Data Cleaning & Preprocessing: Remove duplicates, handle missing values, and normalize text (e.g., remove URLs, hashtags, emojis). Tokenize and prepare text for sentiment analysis. Sentiment Analysis: Implement sentiment classification (positive, negative, neutral) using appropriate libraries (e.g., TextBlob, NLTK, or transformers). Summarize sentiment distribution and trends over time. Exploratory Data Analysis (EDA): Identify patterns, correlations, and insights from the dataset. Formulate and attempt to answer questions such as: How does sentiment vary over time? Are there spikes in positive or negative sentiment linked to specific events? Visualization: Create clear and informative charts (e.g., bar charts, line graphs, word clouds) to support findings. Visualizations should make the analysis easy to understand. Documentation: Include markdown explanations in the Jupyter Notebook for each step. Provide a summary of findings and insights. Deliverables: A Jupyter Notebook containing: All code, comments, and markdown explanations. Data cleaning, analysis, and visualization steps. Data files used for the analysis. Final outputs (charts, tables, and summary insights). Requirements: Proficiency in Python and libraries such as pandas, numpy, matplotlib, seaborn, and NLP tools. Experience with sentiment analysis and data visualization. Ability to write clear, well-documented code. Expected Outcome: A comprehensive analysis that attempts to answer the chosen questions about tweet sentiments. The results may confirm, disprove, or remain inconclusive regarding initial hypotheses, which is acceptable as long as the analysis is thorough and well-presented.